Data Science Projects for Freelancers

We're a professional trader building a private, AI-driven market research engine, and we're looking for a senior Python / machine learning engineer to act as Fractional CTO across a two-phase build.
Phase 1 (this contract): a Financial Market Signal Backtesting Dashboard — a clean measurement harness for trend, momentum, and multi-signal models with walk-forward backtesting.
Scope:
• Python project + GitHub repo
• Historical market-data ingestion (ETFs via yfinance / market-data APIs, SQL storage)
• Baseline models: 200-day trend, time-series & cross-sectional momentum, equal-weight multi-signal
• Walk-forward backtesting with look-ahead-bias controls
• Evaluation metrics: hit rate, Brier score, Sharpe, Sortino, Calmar, max drawdown, turnover, calibration
• A dashboard comparing signals and model performance
Stack: Python, pandas, NumPy, SQL, Python-based dashboard.
Phase 2 (follow-on): we extend the harness into the AI layer — an OODA / weight-of-evidence engine that scores competing market hypotheses. This involves LLM-based signal processing, an AI agent workflow over financial research and macro data, model evaluation / observability (LLMOps), and potentially fine-tuning and RAG over our research library. Strength here shapes who we keep on as Fractional CTO.
You are:
• A senior Python engineer with quantitative finance / backtesting experience
• Strong in machine learning and statistical evaluation
• Comfortable owning architecture as a Fractional CTO — evaluations, regression tests, observability, clean handover
• Bonus for Phase 2: AI agents, LLMs, LLMOps (LangChain / LangGraph, fine-tuning, RAG)

We are looking for an experienced AI Engineer / Data Scientist to design and build the cognitive core of a smart, connected CNC manufacturing machine. The goal is to build an intelligent AI agent (chatbot interface) capable of analyzing embedded hardware telemetry and correlating it with subjective user feedback to optimize manufacturing processes.

You will be responsible for creating a system that ingests raw machine data, queries a static technical knowledge base (PDFs, manuals, studies), and identifies behavioral patterns and limits to improve both individual user profiles and our global community dataset.

Project 1: Backend Framework for OHLC Market Data Ingestion and Storage

We need a backend developer to build a Python/PostgreSQL-based framework for collecting and storing historical OHLC market data for research purposes.

The system should be able to:

Download or import OHLC data in CSV format.
Normalise the data into a consistent schema.
Store securities, tickers, exchanges, sectors and industry classifications.
Load large CSV datasets efficiently into PostgreSQL, preferably using a parallel PostgreSQL loader / COPY-based approach.
Keep raw files and normalised files separately.
Track import jobs, errors and basic data-quality issues.
Provide initial FastAPI endpoints to query securities and historical OHLC data.

Expected stack:

Python
PostgreSQL
FastAPI
SQLAlchemy / asyncpg or similar
Docker / Docker Compose preferred

Deliverables:

Database schema.
Data ingestion scripts.
CSV normalisation process.
PostgreSQL bulk-loading process.
Basic FastAPI service.
Short setup documentation.

The goal is to create a clean backend foundation that can later be used for financial research and statistical analysis.

I’m looking to strengthen my Python fundamentals with an emphasis on data structures. I already know my way around variables, simple loops, and the interpreter, but I need a clearer, more practical grasp of lists, tuples, sets, dictionaries, and the best ways to use them in real code.

Here’s what I have in mind:
• Short, live or recorded walkthroughs that break down each core data structure
• Small, hands-on exercises I can run locally so I see how the concepts behave
• Clear, commented reference scripts I can revisit later

By the end, I want to feel confident choosing the right structure for a task and manipulating it efficiently. If you enjoy guiding learners who sit somewhere between “total newbie” and “intermediate,” let’s talk about the most effective format, schedule, and learning materials.

Skills: Python, Software Architecture, Statistics, Machine Learning (ML), Software Development, Data Science, Programming, Data Analysis

I have a curated dataset of patients’ genetic profiles and need a deep-learning solution that can reliably flag the presence of heart disease. Because the data are entirely genomic, the job begins with thoughtful preprocessing and feature engineering (handling high-dimensional SNPs, normalisation, train/validation split, class-imbalance techniques if required).

My single, overriding success metric is F1 Score; accuracy alone will not do, so the model must be tuned to balance precision and recall. You may choose the exact framework—PyTorch or TensorFlow/Keras are both fine—as long as the final code is clean, reproducible, and GPU-ready.

Deliverables
• End-to-end Python code or notebook that loads the raw genetic data, performs preprocessing, trains the deep neural network, and outputs predictions.
• Saved, versioned model weights plus a short README on how to retrain or fine-tune.
• Evaluation report that highlights F1 Score, precision, recall, and confusion matrix on the held-out test set.
• Brief architecture rationale so I understand why your chosen layers, regularisation, and hyperparameters suit genomic inputs.

I will provide the dataset and any relevant phenotype labels as soon as we start. My priority is a clear, well-documented workflow that I can deploy or extend later, so please keep modularity and readability in mind throughout the project.

Skills: Python, Matlab and Mathematica, Machine Learning (ML), Data Mining, Data Science, Neural Networks, Keras, Data Analysis, Deep Learning, Model Deployment

Company / Brand

CMindset Consulting is launching an AI Governance Academy designed to help professionals, executives, boards, and organizations operationalize responsible AI.

The academy will focus on practical AI governance implementation, including AI risk management, regulatory readiness, AI monitoring, model evaluations, third-party AI risk, GenAI governance, and governance for autonomous agents.

We are looking for a growth and conversion-focused agency to help us position, launch, and scale the academy.

This is not a generic social media engagement. We are looking for a strategic partner that can help turn subject-matter authority into qualified leads, paid enrollments, corporate training opportunities, and long-term brand authority.


About the AI Governance Academy

The AI Governance Academy is built for professionals and organizations that need practical, implementation-ready training on responsible AI and AI governance.

Our target audiences include:

* Risk and compliance professionals
* Legal and privacy teams
* Data science and model risk teams
* AI product and transformation leaders
* Executives and board members
* Consultants and professionals transitioning into AI governance
* Organizations seeking AI governance training for internal teams

The academy is differentiated because it is not theoretical. It focuses on how AI governance actually works inside enterprises: intake, risk tiering, controls, monitoring, evaluations, documentation, regulatory alignment, incident response, and executive oversight.


Engagement Objective

We need an agency to help us build a high-converting go-to-market engine for the AI Governance Academy.

The goal is to move from visibility and authority to conversion and revenue.

The agency should help us:

* Clarify the academy’s positioning and offer structure
* Define the target buyer and learner segments
* Build a conversion-ready funnel
* Create compelling landing pages and sales copy
* Develop lead magnets and webinar/masterclass campaigns
* Launch email nurture sequences
* Build a LinkedIn and newsletter content engine
* Track performance and optimize conversion
* Support enrollment into paid cohorts, workshops, and corporate training programs


Scope of Work

1. Strategy & Positioning

The agency will define the go-to-market strategy for the academy.

Deliverables may include:

* Audience segmentation
* Buyer persona development
* Offer architecture
* Competitive positioning
* Pricing and packaging recommendations
* Messaging framework
* Launch roadmap
* Funnel strategy

The agency should be able to answer:

* Who is most likely to buy first?
* What offer should we launch with?
* What is the clearest transformation we are selling?
* How do we position the academy as practical, credible, and premium?
* How do we sell to both individuals and organizations?

2. Conversion Copywriting

The agency must have strong conversion copywriting capability.

Deliverables may include:

* Academy landing page copy
* Cohort sales page copy
* Corporate training page copy
* Webinar/masterclass registration copy
* Lead magnet copy
* Email nurture copy
* LinkedIn CTA posts
* Sales call booking page copy

We are not looking for generic “AI is transforming the world” messaging. The copy must speak to real enterprise AI governance pain points, including:

* AI projects stuck in pilot mode
* Lack of governance ownership
* Regulatory pressure
* AI monitoring gaps
* Model evaluation challenges
* GenAI and agentic AI risks
* Board and executive accountability
* Translation of frameworks into operating models


3. Funnel Build & Demand Generation

The agency should design and help implement a full lead-generation and conversion funnel.

Expected funnel may include:

LinkedIn / Thought Leadership → Lead Magnet → Email Nurture → Free Masterclass → Paid Academy / Corporate Training

Potential campaign assets include:

* AI Governance Readiness Checklist
* AI Monitoring & Evals Maturity Assessment
* Board AI Oversight Guide
* Responsible AI Operating Model Template
* Free AI Governance Masterclass
* Corporate AI Governance Training Offer
* 6-week AI Governance Academy Cohort

The agency should be able to build the system, not just create isolated content.

4. Content Strategy

The agency will create a content strategy that supports demand generation and conversion.

Content pillars may include:

* AI governance in production
* AI monitoring and evaluations
* Responsible AI operating models
* AI regulatory readiness
* Board and executive AI oversight
* Third-party AI governance
* GenAI governance
* Autonomous agent governance
* Enterprise AI implementation lessons

Deliverables may include:

* Monthly content calendar
* LinkedIn founder-led content
* Newsletter themes
* Webinar topics
* Short-form video ideas
* Repurposing strategy
* Thought leadership campaign themes

Content must position the academy as practical, enterprise-grade, and implementation-focused.


5. Email Marketing & CRM

The agency should set up or improve the email and CRM journey.

Deliverables may include:

* Lead capture setup
* Welcome sequence
* Webinar registration sequence
* Post-webinar nurture sequence
* Enrollment conversion emails
* Corporate training outreach sequence
* Segmentation by audience type
* CRM tagging and pipeline tracking

Preferred tools may include:

* HubSpot
* ConvertKit
* Mailchimp
* ActiveCampaign
* Kajabi
* GoHighLevel
* Airtable
* Notion
* Zapier / Make


6. Landing Page / Website Support

The agency should be able to design and/or direct conversion-focused landing pages.

Pages needed may include:

* AI Governance Academy homepage
* Cohort program page
* Corporate training page
* Free masterclass page
* Lead magnet download page
* Founder / instructor credibility page
* Thank-you page
* Sales call booking page

The pages should be clear, premium, and conversion-oriented.


7. Analytics & Optimization

The agency must track what is working.

Required metrics:

* Landing page conversion rate
* Lead magnet conversion rate
* Webinar registration rate
* Webinar attendance rate
* Email open and click rates
* Sales call booking rate
* Paid enrollment conversion rate
* Cost per lead, where applicable
* Cost per enrollment, where applicable
* Corporate inquiry volume
* Revenue by campaign

The agency should provide regular performance reporting and optimization recommendations.


Ideal Agency Profile

We are looking for an agency with experience in one or more of the following:

* B2B education
* Executive education
* Professional training programs
* Cohort-based courses
* SaaS or tech-enabled services
* AI, data, risk, compliance, legal, cybersecurity, or governance markets
* Founder-led thought leadership
* Conversion funnels and demand generation

The ideal agency understands how to sell expertise-based products, not just consumer products.


Required Capabilities

The agency should have access to the following roles or capabilities:

* Growth strategist / fractional CMO
* Positioning and messaging strategist
* Conversion copywriter
* Demand generation specialist
* Email marketing / CRM specialist
* Landing page designer
* Marketing operations / analytics specialist
* LinkedIn content strategist
* Optional: paid ads specialist
* Optional: video editor / creative producer


What Success Looks Like

Success in the first 90 days means:

* Clear positioning for the AI Governance Academy
* Defined target segments and offer structure
* Conversion-ready landing pages
* Lead magnet and/or webinar funnel launched
* Email nurture sequence live
* LinkedIn and newsletter content strategy active
* First qualified leads generated
* Paid enrollment or corporate training pipeline established
* Dashboard showing funnel performance


Proposed First 90-Day Deliverables

Days 1–30: Strategy & Foundation

* Audit current brand, offer, audience, and assets
* Define target audiences and buyer journeys
* Clarify academy positioning
* Recommend launch offer
* Build messaging framework
* Develop 90-day growth roadmap

Days 31–60: Funnel & Asset Build

* Create lead magnet or masterclass campaign
* Build landing pages
* Write email nurture sequence
* Create LinkedIn content calendar
* Build CRM tagging and tracking
* Prepare launch campaign assets

Days 61–90: Launch & Optimize

* Launch campaign
* Monitor funnel metrics
* Optimize landing pages and emails
* Support webinar/masterclass execution
* Track enrollment and corporate inquiries
* Recommend next campaign cycle

Proposal Requirements

Interested agencies should submit:

1. Agency overview
2. Relevant experience
3. Case studies or examples of similar work
4. Recommended go-to-market approach
5. Proposed 90-day plan
6. Team structure and roles
7. Estimated timeline
8. Pricing model
9. Tools/platforms used
10. Reporting cadence
11. Examples of landing pages, funnels, or email campaigns created


Evaluation Criteria

Agencies will be evaluated based on:

* Understanding of B2B education and expertise-based businesses
* Ability to build conversion funnels
* Strength of positioning and messaging
* Quality of copywriting
* Experience with LinkedIn-led growth
* Ability to track and optimize funnel metrics
* Strategic thinking, not just execution
* Familiarity with AI, governance, risk, compliance, or technical audiences
* Clarity of proposed deliverables
* Speed and quality of execution


Preferred Engagement Model

We are open to:

* 90-day launch engagement
* Monthly retainer
* Project-based funnel build
* Hybrid strategy + execution model

Preference will be given to agencies that can own both strategy and execution, not just provide recommendations.



How to Apply

Please submit your proposal with the subject line:

Growth Agency Proposal — AI Governance Academy

Include relevant examples of prior work, proposed scope, estimated pricing, and the team that would support this engagement.

Project Overview

I already have a working OpenAlgo setup with broker integration, WebSocket, scheduler, and order execution. I need an experienced Python/OpenAlgo developer to implement a new strategy logic called **PMPS V2.0 (Progressive Momentum Pyramid Strategy)**.

This is not a complete application development project. Existing OpenAlgo infrastructure is already available. Only strategy logic and position management need to be developed.

Scope of Work

Develop a production-grade multi-stock strategy that:

* Runs continuously during market hours.
* Supports multiple stocks simultaneously.
* Maintains independent state for each stock.
* Can be deployed on Oracle Cloud, AWS EC2, Docker, or PM2.
* Persists state so that server restarts do not lose positions.

---

## Existing Components (Already Available)

Please reuse the existing OpenAlgo framework:

* API connection
* Broker integration
* WebSocket
* Scheduler
* Order execution
* Existing strategy framework

No modifications are required for:

* Authentication
* Broker layer
* WebSocket
* Scheduler

Only strategy logic must be implemented.

---

## Supported Brokers

Through OpenAlgo:

* ICICI Direct Breeze
* Zerodha
* Angel One
* Upstox
* Dhan

No broker-specific code should be written.

---

## Multi-Stock Support

Example:

```python
STOCKS_LIST = [
"RELIANCE",
"TCS",
"INFY",
"HDFCBANK",
"ICICIBANK"
]
```

Each stock should maintain its own position and state.

---

## Buy Logic

### Buy1

Investment:

₹40,000

### Buy2

Condition:

Price >= Base Price × 1.02

Investment:

₹20,000

### Buy3

Condition:

Price >= Base Price × 1.04

Investment:

₹25,000

### Buy4

Condition:

Price >= Base Price × 1.06

Investment:

₹30,000

Maximum exposure:

₹1,15,000 per stock.

---

## Profit Booking

### Target1

Sell 20%

Condition:

Average Price × 1.05

---

### Target2

Sell 30%

Condition:

Average Price × 1.064

---

### Target3

Sell remaining quantity

Condition:

Average Price × 1.07

Close position.

---

## Stop Loss

Condition:

CMP <= Average Price × 0.90

Action:

* Sell all quantity
* Reset strategy

Maximum loss:

10%

---

## Trailing Stop

Activated after Target1.

Track highest price reached.

Condition:

CMP <= Highest Price × 0.95

Sell remaining quantity.

---

## Time Exit

Maximum holding period:

30 trading days

Condition:

holding_days > 30

Action:

Sell all quantity.

---

## Quantity Normalization

Use integer quantities only.

Examples:

| Raw Qty | Final Qty |
| ------- | --------- |
| 1.3 | 1 |
| 1.5 | 2 |
| 1.7 | 2 |
| 2.5 | 3 |

Implementation:

```python
qty = max(1, round(raw_qty))
```

---

## Position State

Maintain independent state per stock:

```python
{
"base_price":100,
"average_price":102.77,
"highest_price":110,
"total_cost":115000,
"total_qty":1119,

"buy1_done":True,
"buy2_done":True,
"buy3_done":True,
"buy4_done":True,

"target1_done":False,
"target2_done":False,
"target3_done":False,

"trailing_active":False,
"position_open":True
}
```

---

## State Machine

NEW

↓

BUY1_FILLED

↓

BUY2_FILLED

↓

BUY3_FILLED

↓

BUY4_FILLED

↓

POSITION_OPEN

↓

TARGET1_DONE

↓

TRAILING_MODE

↓

TARGET2_DONE

↓

TARGET3_DONE

↓

POSITION_CLOSED

Emergency States:

* STOPLOSS_HIT
* TRAILING_STOP_HIT
* TIME_EXIT

---

## Persistence

Need persistence using one of the following:

Preferred:

* PostgreSQL

Alternative:

* SQLite
* JSON

Strategy should recover automatically after cloud/server restart.

---

## Notifications

Telegram alerts for:

* Buy execution
* Partial profit booking
* Stop loss hit
* Position closed

---

## Deliverables

### Main File

```
pmps_strategy.py
```

### Optional Structure

```
core/
strategy_engine.py
position_manager.py
risk_manager.py
quantity_manager.py

database/
repository.py


## Deployment

Must support:

* Oracle Cloud
* AWS EC2
* Docker
* PM2

Example:

```bash
python pmps_strategy.py
```

---

## Preferred Skills

* Python
* OpenAlgo
* Trading APIs
* PostgreSQL
* Docker

---

Please Apply Only If You Have Experience With

* OpenAlgo
* Algorithmic trading
* Position management
* Multi-stock strategies
* Cloud deployment

Skills: Python, Algorithm, Software Architecture, Statistics, Financial Analysis, Statistical Analysis, Data Science, API, Data Analysis, Trading

We are seeking a skilled data scientist to work on a project involving Large Language Models. The role involves data analysis, model training, and deployment. The ideal candidate will have experience in data mining and be proficient in Python. This is a part-time position with a duration of 1 to 3 months.

We need a developer to integrate AI features into our Shopify store. The system should recommend products based on customer behavior and improve conversions. Experience with AI APIs, Shopify custom apps, and automation workflows is required. The goal is to create a smarter shopping experience and increase sales.

Job Description

I am looking for an experienced Python backend engineer to take a fully written, tested, and documented geospatial scoring platform and deploy it into production.

What is already done:

Complete Python backend codebase — scoring pipeline, REST API, database schema
705-line technical specification document covering every design decision
27-test test suite that passes from day one
Requirements.txt with all dependencies pinned
What you will do:

Deploy infrastructure on AWS or DigitalOcean (PostgreSQL + PostGIS, Redis, FastAPI)
Connect live external data pipelines via public APIs (OpenStreetMap Overpass, eBird Cornell Lab, Copernicus satellite)
Implement Stripe payment processing and automated PDF report delivery
Set up Redis caching, performance optimisation, and monitoring
Ensure security best practices — environment variable management, API key protection
Run the existing test suite and confirm all 27 tests pass in production
Tech stack:

Python 3.11 · FastAPI · PostgreSQL 15 + PostGIS · Redis · Celery · Rasterio · GDAL · Shapely · GeoPandas · OSMnx · NumPy · SciPy · Stripe · AWS EC2/RDS or DigitalOcean · ReportLab (PDF generation)

You are the right fit if:

You have deployed Python FastAPI backends in production before
You are comfortable with geospatial libraries (GDAL, Rasterio, PostGIS)
You understand environment variable security and have never committed secrets to git
You can work independently from a technical specification without hand-holding
You have experience with cloud infrastructure (AWS preferred, DigitalOcean acceptable)
Project scope: This is a fixed-price project with a clear deliverable: the platform running in production, all external data sources connected, Stripe payments live, test suite passing. Estimated 3–5 weeks for a strong engineer.

NDA required before I share the codebase. This is a patent-pending platform — confidentiality is taken seriously.

To be considered, please answer in your proposal:

Have you worked with geospatial libraries (GDAL, Rasterio, PostGIS, OSMnx)? Describe briefly.
Describe a Python backend you have deployed to production in the last 12 months.
What is your estimated timeline and fixed price for this scope?
Budget: Fixed price · USD $2,000–$5,000 depending on experience
Timeline: Start immediately · Target completion 4 weeks
Location: Worldwide · Async-friendly
Experience level: Expert

Research Ignited (ResearchIgnited.com) offers live online AI, STEM, research, aerospace, robotics, entrepreneurship, and AP programs for middle and high school students.

We are looking for a friendly and confident Sales Assistant / Enrollment Advisor to follow up with parents and students who have already expressed interest in our programs. This is primarily a warm-lead role, not cold calling.

Responsibilities
Call interested parents and students
Understand the student’s grade, interests, and goals
Explain programs, schedules, pricing, and outcomes
Recommend the best-fit program
Follow up by phone, text, and email
Track lead status and encourage enrollment
Preferred Qualifications
College undergraduate, graduate student, or recent graduate
IT, computer science, engineering, data science, biology, or other STEM background preferred
Excellent spoken English and professional communication
Comfortable speaking with U.S.-based parents and students
Sales, admissions, tutoring, education, or customer-service experience preferred
Available during U.S. afternoon, evening, or weekend hours
What We Provide
Warm leads
Program training
Call scripts, FAQs, and follow-up templates
Opportunity for ongoing work and performance-based bonuses
Schedule and Compensation

Approximately 5–10 hours per week initially
$15–$25 per hour, depending on experience, with possible enrollment bonuses

Required Voice Recording

Please submit a 60–90 second audio or video recording covering:
Your educational background
Relevant sales, tutoring, admissions, or customer-service experience
How you would explain an AI program to a parent whose 10th-grade student has no prior coding experience
Applications without the requested recording may not be considered.

I have a raw data set that I need translated into clear, actionable insights. At this stage I’m flexible on the tools you use—Python (pandas, NumPy, matplotlib), R, Excel, Power BI, or a mix—so long as the findings are easy for a non-technical stakeholder to understand and the work can be reproduced later.

Here’s what I’d like to walk away with:

• a cleaned, well-structured version of the data (same file type I provide, unless you suggest better)
• an executive-level summary that highlights the most important patterns and any anomalies worth attention
• the working files—code notebooks, pivot tables, or dashboards—so I can trace every step of the analysis

Acceptance criteria: results must reconcile with the source data, any code should run without errors on a standard setup, and the narrative summary should clearly explain what the numbers mean for decision-making.

Let me know your preferred approach and an estimated turnaround, and we can get started right away.

Skills: Python, Excel, Statistics, Statistical Analysis, Data Science, Data Visualization, Data Analysis, Data Management

We are looking for a remote Machine Learning Engineer to help build and deploy production document-AI systems. The work centres on optical character recognition (OCR), document analysis, computer vision, and NLP/LLM components that automate document-heavy operational processes. You will develop and optimise deep-learning models, build end-to-end ML pipelines (data ingestion, feature engineering, training, evaluation, inference), and deploy them using Docker, Kubernetes, and modern MLOps practices. Essential skills: strong Python; TensorFlow and/or PyTorch; OpenCV; and hands-on experience with OCR and computer vision. Familiarity with LLMs and document-analysis techniques (e.g., TrOCR) is a strong plus. This is an ongoing, fully remote contract role with flexible hours.

I am looking for an experienced Power BI developer to update an existing Power BI dashboard.

The dashboard contains multiple pages and currently uses imported data. I need someone to review the existing data model and convert the reports to use DirectQuery against the database while maintaining the current functionality, calculations, filters, and visualizations.

Scope of Work:
• Review the existing Power BI report and data model.
• Convert imported tables/datasets to DirectQuery.
• Update queries, relationships, and measures as needed.
• Ensure all report pages continue to function correctly.
• Optimize performance and troubleshoot any DirectQuery limitations.
• Test and validate report accuracy against the source database.

Requirements:
• Strong experience with Power BI.
• Experience migrating reports from Import Mode to DirectQuery.
• Strong SQL and database knowledge.
• Experience with Power Query, DAX, and data modeling.
• Ability to optimize Power BI report performance.

When Applying, Please Include:
• Examples of similar Power BI projects.
• Experience with DirectQuery implementations.
• Estimated time required to complete the work.

Skills Required:
Power BI, DirectQuery, SQL, DAX, Power Query, Data Modeling, Performance Optimization.

Title: Research and Design of Automated Trading Strategies (Stocks, ETFs, Forex, Crypto, Sports Betting Exchanges)


I am looking for experienced quantitative traders, algorithmic traders, data scientists, betting exchange traders, and trading system developers.

This project is NOT initially for coding a trading bot. The first phase is a paid research and strategy review project.

I want proposals covering any of the following:

• Stock market trading systems
• ETF trading systems
• Forex trading systems
• Cryptocurrency trading systems
• Futures trading systems
• Options trading systems
• Sports betting exchange systems (Betfair API preferred)

For each strategy, please provide:

1. Strategy description
2. Markets traded
3. Timeframe (scalping, intraday, swing, long-term)
4. Expected annual return
5. Maximum drawdown
6. Historical backtest period
7. Number of trades
8. Win rate
9. Profit factor
10. Risk management methodology
11. Broker/API used
12. Live trading evidence if available
13. Estimated development cost
14. Estimated maintenance cost

Preference will be given to candidates who can provide:

• Source code ownership options
• Full intellectual property transfer
• Backtesting reports
• Myfxbook, broker statements, or verified performance
• Betfair API experience
• Python, C#, Java, or cloud-based implementations

Important:

I am not interested in guaranteed returns or marketing claims.

Any proposal claiming very high returns without supporting data will be rejected.

I am specifically interested in strategies that can be independently verified through historical testing and paper trading.

Future phases may include:

• Building a full trading platform
• Automated execution
• Risk management dashboard
• Portfolio management
• Betfair trading automation
• Full source code purchase

Please include examples of previous work and screenshots of performance metrics where possible.

Skills: PHP, Python, Software Architecture, Statistics, Market Research, R Programming Language, Financial Analysis, Data Science, API Development, Backtesting

AI Agent Evaluator
Remote · Contract · Flexible Hours
We're hiring evaluators to help assess and benchmark cutting-edge AI agents. This is hands-on work at the frontier of agentic AI — not prompt writing, not basic QA. You will be designing complex real-world tasks, running them across multiple large language models, and producing the structured evaluations that shape how the next generation of AI agents are trained.
What You'll Do
You will design multi-stage agent tasks across real-world domains including health, finance, productivity, relationships, and personal exploration. Each task must require the agent to coordinate across multiple tools and systems, manage persistent state, handle realistic friction points, and produce a verifiable final artifact. Tasks that can be completed in a short, linear interaction are not complex enough for this project.
You will run each task prompt across two AI models inside the OpenClaw environment, extract the full agent trajectories, and compare how each model planned, reasoned, used tools, and arrived at its final output.
You will build binary evaluation rubrics that are atomic, objective, and self-contained — weighted on a scale from -5 to +5 — to measure task completion, instruction following, tool use, agent behavior, factuality, and safety. At least one negative-weight criterion is required per rubric set.
You will annotate safety failures across seven domains including high-stakes actions, private data handling, ambiguous instructions, third-party prompt injections, and over-refusal. Each failure must be categorized by type, trajectory step, and action tier.
You will write pytest-based unit tests in a verifiers.py file that validate the agent's final system state using snapshots.json as ground truth — confirming outcomes, not just intent.
You will select the best-performing model trajectory, clone it, and iterate with the model until it passes all rubrics, producing a polished silver trajectory as the reference trace.
You Must Be Able To

Design complex multi-stage agentic tasks with real friction, constraint logic, and verifiable outcomes
Write evaluation rubrics that are atomic, self-contained, and expose meaningful differences between models
Annotate safety failures with the correct category from a defined failure taxonomy and assign the correct action tier
Read and reason critically about full agent trajectories
Write and validate basic Python unit tests using pytest
Source every task idea from a real, publicly available online post about OpenClaw — no invented scenarios

Strong Background In
AI evaluation, rubric-based assessment, data annotation, agentic AI systems, AI safety concepts, prompt engineering, Python and pytest, and structured critical thinking.
Important
This is not a prompt-writing role or a basic chatbot quality assurance position. You are evaluating whether AI agents can architect solutions, coordinate tools, manage persistent state, and behave safely under real-world constraints. If a task can be solved without architectural reasoning or meaningful tool coordination, it does not meet the bar for this project.

Skills: Excel, Technical Writing, Machine Learning (ML), Data Science, Data Visualization, AI (Artificial Intelligence) HW/SW, Computer Vision, Software Engineering, Prompt Engineering

analyze a screen. Specifically, if a value on the screen is zero, it should press the space key. If the value is greater than zero, it should press the down key. I don’t need to integrate this into an app; it just needs to run by analyzing what’s on the screen itself. Once I have this script, I’ll provide it to them to use."

Masai-Live is a collaborative, real-time clone or application inspired by platforms like Hotstar, YouTube Live, or a customized learning management system (LMS) portal like Masai School's own platform.

It is typically designed to handle media streaming, video content management, user enrollment, or live event tracking.

Core Key Features (What your project likely does)
User Authentication & Authorization:

Secure signup and login functionality for different roles (e.g., Student/Viewer vs. Admin/Instructor) using JWT (JSON Web Tokens) or Firebase.

Video & Live Stream Cataloging:

A dynamic home dashboard showcasing ongoing live streams, recorded lectures/videos, categories (e.g., Web Development, Data Science), and trending content.

Admin Dashboard / Content Management:

A full CRUD (Create, Read, Update, Delete) portal where admins or instructors can schedule a live session, add new video links, update titles, or delete past sessions.

Interactive Learning / Filtering:

Advanced search functionality, sorting (by date, popularity), and filtering content by tags or modules.

Responsive User Interface:

A clean, modern UI optimized for desktop, tablet, and mobile viewing to ensure an optimal user experience during video playback.

Typical Tech Stack Used
Given standard Masai School curriculums, your project likely uses one of these two major stacks:

Frontend: React.js, Redux (for global state management), HTML5, CSS3 / Tailwind CSS / Chakra UI (for rapid styling and responsive layout).

Backend (if Full-Stack): Node.js, Express.js, and MongoDB (Mongoose) to store user data, session logs, and video metadata.

Alternative Frontend Only: HTML, CSS, JavaScript (ES6+), using a mock backend like JSON-Server or local storage to simulate database interactions.

Skills: PHP, JavaScript, Website Design, MySQL, HTML5, Node.js, React.js, MongoDB

I am looking for a Python and SAS expert. Knowledge of data bricks helpful. Experience as a Statistician or programmer in both Python and SAS is helpful.

New to Python-Looking for a consultant to compile/recommend a list of Python libraries suited for statistics and data analysis. Individual should have SAS experience /Statistics to be able to find equivalent packages in Python.

List must include the following;

Package in Python that is an equivalent to Data cleaning in SAS for very large data sets
Python package-to import and export files to Excel
Binwidth in Python that is a SAS equivalent
Graphics/histograms/plots, visualizations
Survey select procedure in SAS equivalent in Python
Random Number generation in SAS equivalent in Python
Other packages for statisticians to complete their work that is equivalent to the SAS STAT package procedures.

Include steps on how to install the packages and the location to get the files from.

Any recommended memory size for laptops.

Project may happen in phases. Phase 2 may include training on basic Python syntax after the list has been compiled.

Math for Data Analytics and Visualizing Distribution
1. Consider the following matrix:

Matrix
57 67 35 47
59 62 77 22
24 44 72 21

-What are its dimensions?
-Create a numpy array corresponding to this matrix.
-Create a numpy array of shape (5,3) and fill it with random integers.

2. Using pandas,

-Load the Walmart sales dataset
-Select store 1 and plot its weekly sales over time. Make sure to adhere to visualization guidelines in your plot.
-Identify trends, peaks, and valleys in the sales.
-Modify your plot to also display the occurrence of holidays.

3. Using the Walmart sales data for store 1, you will now create a simple probabilistic model for sales

-Create an additional column, named ‘Week Number’ in your dataframe that translates the ‘Date’ to the week of the year.
-(For example, 2/5/2010 should be 5, and 7/29/2011 should be 30. Hint: pd.to_datetime(*Date Column*, format='%d-%m-%Y').dt.isocalendar().week)
-Compute the average sales per week of the year.
-Plot the result.
-Identify trends, peaks, and valleys in the plot.

Skills: Data Processing, Statistical Analysis, Data Science, Data Analytics, NumPy, Data Visualization, Data Analysis, Pandas

AI Agent Evaluator
Remote · Contract · Flexible Hours
We're hiring evaluators to help assess and benchmark cutting-edge AI agents. This is hands-on work at the frontier of agentic AI — not prompt writing, not basic QA. You will be designing complex real-world tasks, running them across multiple large language models, and producing the structured evaluations that shape how the next generation of AI agents are trained.
What You'll Do
You will design multi-stage agent tasks across real-world domains including health, finance, productivity, relationships, and personal exploration. Each task must require the agent to coordinate across multiple tools and systems, manage persistent state, handle realistic friction points, and produce a verifiable final artifact. Tasks that can be completed in a short, linear interaction are not complex enough for this project.
You will run each task prompt across two AI models inside the OpenClaw environment, extract the full agent trajectories, and compare how each model planned, reasoned, used tools, and arrived at its final output.
You will build binary evaluation rubrics that are atomic, objective, and self-contained — weighted on a scale from -5 to +5 — to measure task completion, instruction following, tool use, agent behavior, factuality, and safety. At least one negative-weight criterion is required per rubric set.
You will annotate safety failures across seven domains including high-stakes actions, private data handling, ambiguous instructions, third-party prompt injections, and over-refusal. Each failure must be categorized by type, trajectory step, and action tier.
You will write pytest-based unit tests in a verifiers.py file that validate the agent's final system state using snapshots.json as ground truth — confirming outcomes, not just intent.
You will select the best-performing model trajectory, clone it, and iterate with the model until it passes all rubrics, producing a polished silver trajectory as the reference trace.
You Must Be Able To

Design complex multi-stage agentic tasks with real friction, constraint logic, and verifiable outcomes
Write evaluation rubrics that are atomic, self-contained, and expose meaningful differences between models
Annotate safety failures with the correct category from a defined failure taxonomy and assign the correct action tier
Read and reason critically about full agent trajectories
Write and validate basic Python unit tests using pytest
Source every task idea from a real, publicly available online post about OpenClaw — no invented scenarios

Strong Background In
AI evaluation, rubric-based assessment, data annotation, agentic AI systems, AI safety concepts, prompt engineering, Python and pytest, and structured critical thinking.
Important
This is not a prompt-writing role or a basic chatbot quality assurance position. You are evaluating whether AI agents can architect solutions, coordinate tools, manage persistent state, and behave safely under real-world constraints. If a task can be solved without architectural reasoning or meaningful tool coordination, it does not meet the bar for this project.

Skills: Machine Learning (ML), Data Science, Data Visualization, AI (Artificial Intelligence) HW/SW, Computer Vision, Software Engineering, Prompt Engineering

AI Agent Evaluator
Remote · Contract · Flexible Hours
We're hiring evaluators to help assess and benchmark cutting-edge AI agents. This is hands-on work at the frontier of agentic AI — not prompt writing, not basic QA. You will be designing complex real-world tasks, running them across multiple large language models, and producing the structured evaluations that shape how the next generation of AI agents are trained.
What You'll Do
You will design multi-stage agent tasks across real-world domains including health, finance, productivity, relationships, and personal exploration. Each task must require the agent to coordinate across multiple tools and systems, manage persistent state, handle realistic friction points, and produce a verifiable final artifact. Tasks that can be completed in a short, linear interaction are not complex enough for this project.
You will run each task prompt across two AI models inside the OpenClaw environment, extract the full agent trajectories, and compare how each model planned, reasoned, used tools, and arrived at its final output.
You will build binary evaluation rubrics that are atomic, objective, and self-contained — weighted on a scale from -5 to +5 — to measure task completion, instruction following, tool use, agent behavior, factuality, and safety. At least one negative-weight criterion is required per rubric set.
You will annotate safety failures across seven domains including high-stakes actions, private data handling, ambiguous instructions, third-party prompt injections, and over-refusal. Each failure must be categorized by type, trajectory step, and action tier.
You will write pytest-based unit tests in a verifiers.py file that validate the agent's final system state using snapshots.json as ground truth — confirming outcomes, not just intent.
You will select the best-performing model trajectory, clone it, and iterate with the model until it passes all rubrics, producing a polished silver trajectory as the reference trace.
You Must Be Able To

Design complex multi-stage agentic tasks with real friction, constraint logic, and verifiable outcomes
Write evaluation rubrics that are atomic, self-contained, and expose meaningful differences between models
Annotate safety failures with the correct category from a defined failure taxonomy and assign the correct action tier
Read and reason critically about full agent trajectories
Write and validate basic Python unit tests using pytest
Source every task idea from a real, publicly available online post about OpenClaw — no invented scenarios

Strong Background In
AI evaluation, rubric-based assessment, data annotation, agentic AI systems, AI safety concepts, prompt engineering, Python and pytest, and structured critical thinking.
Important
This is not a prompt-writing role or a basic chatbot quality assurance position. You are evaluating whether AI agents can architect solutions, coordinate tools, manage persistent state, and behave safely under real-world constraints. If a task can be solved without architectural reasoning or meaningful tool coordination, it does not meet the bar for this project.

Skills: Machine Learning (ML), Data Science, Data Visualization, AI (Artificial Intelligence) HW/SW, Computer Vision, Software Engineering, Prompt Engineering

I have a sizeable EEG dataset that needs to be transformed into a working epileptic-seizure detection and short-term prediction pipeline. The workflow I have in mind is clear:

• Data preprocessing: begin with classic EEG Filtering and follow with signal Normalization to standardise every trial.

• Feature extraction: apply a Wavelet Transform to capture the relevant time–frequency patterns that precede a seizure.

• Dimensionality reduction: reduce the resulting feature space intelligently so the model is both fast and interpretable.

• Modelling: my first choice is a Random Forest classifier/regressor for both detection and prediction tasks; however, if you can show convincing evidence that a complementary deep-learning block will boost performance without sacrificing interpretability, I am open to discussion.

• Explainable AI: integrate SHAP or LIME so each prediction can be traced back to the contributing EEG channels, scales, or time-windows.

• Performance evaluation: deliver detailed metrics (accuracy, sensitivity, specificity, F1, ROC-AUC, false-alarm rate per hour) on a solid cross-validation split and, if possible, an unseen hold-out set.

Deliverables must include
– Clean, well-commented source code (Python preferred: NumPy, Pandas, MNE, scikit-learn, SHAP/LIME, etc.)
– A concise technical report that documents preprocessing choices, feature engineering, modelling decisions, and performance tables/plots
– Reproducible experiments packaged in notebooks or scripts with clear run instructions
– A read-me explaining environment setup and how to deploy or further train the system.

I value transparent, modular code, reproducibility, and plain-language explanations of the model’s decisions. If this matches your expertise, let’s make EEG more predictable together.

Skills: Python, Matlab and Mathematica, Algorithm, Statistics, Machine Learning (ML), Statistical Analysis, Data Science, Artificial Intelligence

About the project
We run account management for e-commerce/dropshipping clients. A large part of the work is recurring workflows: order checks, delay and compensation handling, disputes, stock control, and reporting. We're automating these. You'll be responsible for the workflow automations and data analyses end-to-end — a mix of regular automation and AI-driven actions.

This is one of two parallel tracks. A separate developer is building a WhatsApp workspace ("skin") for our account managers, and your automations will ultimately surface inside that workspace. The two tracks are built separately but must integrate cleanly, so well-defined interfaces and good coordination matter.

Who you are
- AI-first developer, strong in both Python and AI, with real coding skills under the hood.
- You build microservice-based, not monolithic. Our hard requirement: each automation and service is isolated so that if one breaks, the rest keeps running. You design with clean separation and replaceable parts.
- Comfortable with a mix of regular automation and AI actions — you know when a rule-based approach is right and when to reach for AI, and you can build the AI-judgment parts well (e.g. "is this really out of stock or just delayed?", summarizing, drafting).
- Able to work with APIs and handle real data volumes efficiently (order datasets can get large).
- Responsible for work end-to-end: architecture, build, testing, delivery.
- Good communicator; we work closely (WhatsApp + 1/2 weekly calls), with detailed workflow docs provided.

What you'll build
- Workflow automations: rule-based work like filtering, status checks, reminders, and reporting, driven off our platform API.
- AI actions and data analyses: the judgment-based tasks and analyses over large datasets.
- A clean, maintainable, microservice-based setup that plugs into our wider system — your automations will connect to and surface inside the WhatsApp workspace built by the other developer, so clean integration points are part of the job.

Good to know
The AI needs to run hosted (via API) to start, built behind clean adapters so some parts can move to local/self-hosted models later where it makes sense. Experience with both hosted AI APIs and running/optimizing local/open-source models is a real plus.

How we work
- Task-by-task roadmap, detailed workflow docs, no sprints — you build, we review per delivery via demos and testing.
- You report to our project lead, who coordinates everything (and coordinates between the two tracks).

To apply tell us briefly: (1) where you've combined rule-based automation with AI, and how you decided the split; (2) an example of a microservice-based system you built end-to-end, ideally one that had to integrate with another system or team's work; (3) your experience with hosted AI APIs and/or self-hosted models. Short and concrete beats long and generic.

• Describe a project where you mixed rule-based automation with AI actions. How did you decide which parts were rules and which needed AI?
• Have you worked with both hosted AI APIs and self-hosted/local open-source models? Briefly, what's your experience with each?
• Describe a microservice-based system you built. How did you handle one service failing without breaking the rest?
• Include a link to your GitHub and/or a Python/data project you owned end-to-end.

Guarding the Marketplace: AI-Driven Detection of Return Fraud and Review Manipulation in E-commerce

6,000–8,000 words, formatted for a peer-reviewed journal in marketing/e-commerce or applied AI.


Objective: Produce an original paper examining how AI and machine learning can detect two major e-commerce trust-and-safety threats: return fraud (e.g., wardrobing, empty-box returns, serial returners, receipt fraud) and review manipulation (e.g., fake reviews, paid review rings, bot-generated ratings). The paper must position trust-and-safety as a distinct, underexplored angle relative to the authors’ prior work on personalization, search advertising, and CLV prediction — referencing those only as adjacent background.

Required sections:

• Abstract (200–250 words) with 5–6 keywords
• Introduction: scale and cost of return fraud and fake reviews to retailers and consumer trust
• Literature review (15–25 recent sources, 2023–2026 preferred), covering fraud detection ML

NLP for fake-review detection, and anomaly detection
• Taxonomy of return fraud and review manipulation types
• Technical framework: relevant AI/ML approaches — supervised classifiers (Random Forest, Gradient Boosting), anomaly detection, graph-based methods for detecting coordinated review rings, and NLP/LLM-based fake-review detection
• Analysis of features and signals used for detection (behavioral, transactional, linguistic, network)
• Evaluation considerations: precision/recall trade-offs, false positives and customer experience impact, adversarial evasion
• Ethical and practical implications: consumer fairness, false accusations, privacy, and regulatory context
• Limitations and future research
• References in the target journal’s citation style

Methodology: Conceptual/analytical review with a proposed detection framework. Optionally include an illustrative case study or a small simulated dataset demonstrating one detection approach (e.g., a fake-review classifier or a returns-anomaly model). Note clearly whether any primary data or simulation is expected; otherwise treat as a conceptual review with a proposed framework.

Tone & style: Academic, evidence-based, neutral. Original prose only — no plagiarism, no AI-detectable boilerplate, all claims cited. Include an ORCID placeholder for each author.

Deliverables: Editable Word document, a plagiarism/originality report, and a reference list with working links/DOIs.

Skills: Research Writing, Machine Learning (ML), Statistical Analysis, Data Science, Data Analytics, Data Analysis, Fraud Detection, Predictive Analytics, Natural Language Processing, AI Development

I'm looking for an experienced Azure developer to build a data analytics platform focused on predictive analytics and data visualization. The platform will need to integrate with various APIs.

Key Requirements:
- Predictive Analytics: Implement models to forecast trends and insights.
- Data Visualization: Create intuitive dashboards and visual reports.
- API Integration: Seamlessly connect with multiple data sources via APIs.

Ideal Skills and Experience:
- Strong expertise in Azure services, especially related to data analytics.
- Proficiency in predictive modeling and data visualization tools.
- Experience in API integration and handling complex data flows.

If you have a proven track record in building similar platforms, I would love to hear from you. Please provide examples of your work and any relevant certifications.

Skills: Data Processing, Azure, Data Science, Data Analytics, Data Visualization, API Integration, Predictive Analytics, Data Management

I need a Power BI developer for a quick turn-around dashboard.

# Job Description: LLM Data Scientist / AI Engineer – Agentic AI & RAG

## Employment Type



Full-time

3 -12 months depending on performance


## Role Overview



We are hiring an LLM Data Scientist / AI Engineer to help build advanced AI products using Large Language Models, Retrieval-Augmented Generation, agentic AI workflows, and applied machine learning. This role is ideal for someone who can combine strong data science skills with hands-on engineering ability to design, evaluate, and deploy intelligent AI systems.



The candidate will work on building AI agents, RAG pipelines, model evaluation frameworks, prompt optimization, fine-tuning workflows, and production-ready LLM applications.



## Key Responsibilities



* Design, build, and improve LLM-powered applications using RAG, agentic workflows, and AI orchestration frameworks.

* Build retrieval pipelines using embeddings, vector databases, hybrid search, reranking, and knowledge indexing.

* Develop AI agents capable of tool use, reasoning, planning, workflow automation, and multi-step task execution.

* Experiment with prompt engineering, prompt optimization, guardrails, and structured outputs.

* Fine-tune or adapt open-source and commercial LLMs

* Build evaluation frameworks for LLM quality, hallucination detection, retrieval accuracy, response relevance, latency, and cost.

* Work with product and engineering teams to convert business problems into AI solutions.

* Deploy LLM applications using APIs, microservices, cloud platforms, and scalable inference systems.

* Monitor production AI systems and continuously improve accuracy, safety, reliability, and performance.

* Stay updated with the latest advancements in LLMs, RAG, AI agents, and applied generative AI.



## Required Qualifications



* Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, AI, Statistics, Engineering, or a related field.

* 2+ years of experience in data science, machine learning, AI engineering, or applied NLP.

* Hands-on experience building applications using LLMs such as OpenAI, Anthropic, Gemini, Llama, Mistral, Qwen, or similar models.

* Strong Python programming skills.

* Experience with RAG pipelines, embeddings, vector databases, and retrieval systems.

* Experience with at least one framework such as LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, Haystack, or similar.

* Good understanding of machine learning, NLP, model evaluation, and data preprocessing.

* Experience working with APIs, databases, cloud services, and production software systems.

* Ability to analyze model outputs, debug failures, and improve system quality.



## Preferred Qualifications



* Experience with vector databases such as Pinecone, Weaviate, Milvus, FAISS, Qdrant, Chroma, or Azure AI Search.

* Experience with cloud platforms such as Azure, AWS, or Google Cloud.

* Experience with model serving or inference frameworks such as vLLM, TGI, Triton, Ray Serve, or FastAPI.

* Experience building production-grade AI agents, chatbots, copilots, document intelligence systems, or workflow automation tools.

* Knowledge of LLM safety, guardrails, prompt injection prevention, and responsible AI practices.

* Experience with MLOps, CI/CD, monitoring, observability, and model performance tracking.



## Technical Skills



* Python, SQL, APIs, FastAPI

* LLMs, NLP, embeddings, RAG, AI agents

* Vector databases and search systems

* Prompt engineering and LLM evaluation

* Fine-tuning and preference optimization

* Cloud deployment and scalable AI systems

* Data analysis, experimentation, and model monitoring



## Ideal Candidate



The ideal candidate is hands-on, curious, and comfortable working in a fast-moving AI environment. They should be able to quickly prototype ideas, evaluate model performance, and convert AI research into practical products. They should understand both the data science side and the engineering side of building reliable LLM applications.



## Nice to Have



* Experience in enterprise AI, document processing, customer support automation, search, recommendation, or workflow automation.

* Experience building multi-agent systems or tool-using agents.

* Experience optimizing LLM cost, latency, and inference performance.

* Experience working with structured and unstructured data sources.



## Employment Type



Full-time

3months

## Location



Remote

Skills: Python, Prompt Engineering, Natural Language Processing, AI Agents, Retrieval-Augmented Generation (RAG)

# Job Description: LLM Data Scientist / AI Engineer – Agentic AI & RAG



## Role Overview



We are hiring an LLM Data Scientist / AI Engineer to help build advanced AI products using Large Language Models, Retrieval-Augmented Generation, agentic AI workflows, and applied machine learning. This role is ideal for someone who can combine strong data science skills with hands-on engineering ability to design, evaluate, and deploy intelligent AI systems.



The candidate will work on building AI agents, RAG pipelines, model evaluation frameworks, prompt optimization, fine-tuning workflows, and production-ready LLM applications.



## Key Responsibilities



* Design, build, and improve LLM-powered applications using RAG, agentic workflows, and AI orchestration frameworks.

* Build retrieval pipelines using embeddings, vector databases, hybrid search, reranking, and knowledge indexing.

* Develop AI agents capable of tool use, reasoning, planning, workflow automation, and multi-step task execution.

* Experiment with prompt engineering, prompt optimization, guardrails, and structured outputs.

* Fine-tune or adapt open-source and commercial LLMs

* Build evaluation frameworks for LLM quality, hallucination detection, retrieval accuracy, response relevance, latency, and cost.

* Work with product and engineering teams to convert business problems into AI solutions.

* Deploy LLM applications using APIs, microservices, cloud platforms, and scalable inference systems.

* Monitor production AI systems and continuously improve accuracy, safety, reliability, and performance.

* Stay updated with the latest advancements in LLMs, RAG, AI agents, and applied generative AI.



## Required Qualifications



* Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, AI, Statistics, Engineering, or a related field.

* 2+ years of experience in data science, machine learning, AI engineering, or applied NLP.

* Hands-on experience building applications using LLMs such as OpenAI, Anthropic, Gemini, Llama, Mistral, Qwen, or similar models.

* Strong Python programming skills.

* Experience with RAG pipelines, embeddings, vector databases, and retrieval systems.

* Experience with at least one framework such as LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, Haystack, or similar.

* Good understanding of machine learning, NLP, model evaluation, and data preprocessing.

* Experience working with APIs, databases, cloud services, and production software systems.

* Ability to analyze model outputs, debug failures, and improve system quality.



## Preferred Qualifications



* Experience with vector databases such as Pinecone, Weaviate, Milvus, FAISS, Qdrant, Chroma, or Azure AI Search.

* Experience with cloud platforms such as Azure, AWS, or Google Cloud.

* Experience with model serving or inference frameworks such as vLLM, TGI, Triton, Ray Serve, or FastAPI.

* Experience building production-grade AI agents, chatbots, copilots, document intelligence systems, or workflow automation tools.

* Knowledge of LLM safety, guardrails, prompt injection prevention, and responsible AI practices.

* Experience with MLOps, CI/CD, monitoring, observability, and model performance tracking.



## Technical Skills



* Python, SQL, APIs, FastAPI

* LLMs, NLP, embeddings, RAG, AI agents

* Vector databases and search systems

* Prompt engineering and LLM evaluation

* Fine-tuning and preference optimization

* Cloud deployment and scalable AI systems

* Data analysis, experimentation, and model monitoring



## Ideal Candidate



The ideal candidate is hands-on, curious, and comfortable working in a fast-moving AI environment. They should be able to quickly prototype ideas, evaluate model performance, and convert AI research into practical products. They should understand both the data science side and the engineering side of building reliable LLM applications.



## Nice to Have



* Experience in enterprise AI, document processing, customer support automation, search, recommendation, or workflow automation.

* Experience building multi-agent systems or tool-using agents.

* Experience optimizing LLM cost, latency, and inference performance.

* Experience working with structured and unstructured data sources.

Skills: Data Science, NLP, LLM Prompt Engineering

I need a Spatial Data Scientist for advanced GIS tasks. The project involves:

- GIS Mapping: Use QGIS for topographic, thematic, and network mapping.
- Nearest-Neighbor Analysis: Analyze spatial relationships using relevant datasets (type to be confirmed).
- Paleomagnetic Correlation: Integrate paleomagnetic data with GIS for geological insights.

Ideal Skills & Experience:
- Proficient in QGIS
- Strong background in GIS mapping techniques
- Experience in Nearest-Neighbor Analysis
- Knowledge of paleomagnetic data and its applications
- Strong analytical skills and attention to detail

Please provide samples of similar work.

Skills: Cartography & Maps, Geolocation, Geospatial, Data Science, Geographical Information System (GIS), Data Visualization, Data Analysis, Data Integration

Hello,

I am looking to hire approximately 4 skilled AI/ML research assistants for long-term collaboration. This first project will be a trial collaboration, and successful candidates may receive continuous future work with improved rates over time.

The work must follow academic and professional integrity standards. The selected researcher will support our team with dataset-based experiments, coding, result analysis, literature organization, figures/tables, proofreading, editing, and manuscript improvement. All contributions must be original, transparent, and properly documented.

The ideal candidate should have a strong background in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field. You should understand how to run experiments on real datasets and how to present experimental results clearly and accurately.

Main responsibilities may include:

* Implementing AI/ML experiments on provided or public datasets
* Running experiments on platform Vast.ai
* Saving code, logs, outputs, and results in an organized and reproducible format
* Preparing result tables, performance comparisons, graphs, and analysis notes
* Helping with literature search and reference organization
* Editing and improving manuscript sections written by our team
* Checking grammar, flow, technical clarity, formatting, and consistency
* Supporting revisions based on feedback from the senior researcher or reviewers

The candidate should understand the structure of a research manuscript, including:

* Abstract
* Introduction
* Literature Review
* Methodology
* Experimental Setup
* Results and Analysis
* Discussion
* Conclusion
* References

Our team will provide support and resources, including:

* Clear research direction and task instructions
* Vast.ai GPU credits
* Guidance from a senior researcher
* Feedback on progress and results
* Support for technical review and manuscript improvement

Please apply only if you have real experience with AI/ML experiments and technical research support. In your proposal, please include:

1. Your educational background.
2. Your experience with AI/ML experiments.
3. Examples of datasets you have worked on.
4. Whether you have experience with medical imaging, cybersecurity, or similar datasets.
5. Whether you have used Vast.ai (gpu workspace like google colab).
6. Any published papers, thesis work, code repositories, or research-related samples.
7. A short explanation of how you ensure experiments are reproducible and results are accurate.

This is a long-term opportunity for serious researchers who can work responsibly, communicate clearly, and deliver original, well-documented technical work.